Are you good at programming, statistics and/or machine learning? Are you wondering how to demonstrate your ability in these skills to potential employers? If your answer to these questions is a resounding yes, then this article is just for you. Here’s how you can land the hottest jobs with the perfect data science portfolio!

Why Create a Data Science Portfolio

There are plenty of aspiring data scientists, who have the necessary skills to land the best data science jobs, but don’t have a portfolio. These professionals are missing out on the chance to showcase their abilities to hiring manages by not investing the time to develop a portfolio.

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Remember, your data science portfolio is your proof of work. It helps hiring managers learn what you can bring to the table. You must help them to help you by creating a data science portfolio.

What to Include

What is it that employers want to see in a data science portfolio? The real-world problems that you solved. More importantly, your solutions or how you solved them. Employers want to see you use data creatively – how you engage with data and go beyond the surface level that is the requirement of the assignment.

To this end, avoid including any project that may be deemed to be common like the data set on income from the census or the likes. Include projects with interesting and unique data sets. One way to access interesting data sets is by scraping the web.

Make sure your data science portfolio demonstrates plenty of real-world experience. This does not necessarily translate to real-work experience, so don’t worry if you’re just starting out or haven’t had much of it.

Projects You Worked on Independently

Use any projects that you worked on independently to demonstrate to the employer that you are a go-getter, who doesn’t wait for opportunities but creates them for yourself and others.

Tell them why you were drawn to the project you worked on independently, what started it, why you decided that it was worth working on, and most importantly, what you achieved.

Tell them what you learned from working on the project and how all of it relates to the grander scheme of things. All this information is worthwhile. And guess what, all of it counts as experience.

Work You Did for The Community

Few things in the world are more endearing and appealing than benevolence. If you worked hard on developing an open-source program or code, or painstakingly wrote answers on Stack Overflow, don’t be shy to share it.

These are great examples of you working for the community. And there is nothing like being useful to the data science or developer community.

Work that you do for the public can be beneficial whether you include them in your data science portfolio or not. Developers and data scientists encounter issues too. Much like the rest of us, they too turn to the internet for answers.

If you can help them find a solution to their problems, then chances are they might want to interact, engage or even work with you. At the very least, they will have their eyes on you knowing that they can call on you if they need you.

Kaggle Competitions

Granted that Kaggle competitions don’t involve the important tasks of data collection and cleaning. Nonetheless, experience of participating in these competitions are useful in demonstrating your analytical and modelling skills.

Kaggle competitions cannot be the sole experience you have in your data science portfolio. But, they can be complementary experiences that you can include in your data science portfolio to enhance it.

Capstone Project or Thesis

If you have had any training that required you to work on a capstone project, include the details of that project in your data science portfolio. Capstone projects generally require you to use most of the skills that are valuable in the data science cycle and can be great indicators of your ability to see through a project from the start to its finish.

If you’re training was more academic, be sure to include your thesis. Once again, emphasize on the general need of your thesis, how you worked on it, what you achieved with it, and what you learned from it.

Tips for Resume

If you have a data science portfolio, chances are employers will find it through your resume. A resume is a clear and precise description of your qualifications, and more specifically, what makes you the perfect candidate for any job position.

The secret to having an effective resume is to make every line and word you include count. If you pay close attention to these details, you are more likely to make a lasting impression. Remember, hiring managers go through scores of resumes. So, don’t bore them with fluff or irrelevant stuff.

Keep it Short

A one-page resume is already plenty. It is enough space to cover all the essentials and not too long for a quick inspection from the hiring manager.

Avoid Fluff

Remove any content that does not add value like your ‘objective’. These are overstated and too superficial for anyone’s liking.

Include Coursework & Training

Be sure to include any relevant coursework or training. These tend to be reassuring for the employer.

Focus on Skills

List all relevant skills, preferably in the order of your proficiency in them but do not rate your ability explicitly. A description of your capabilities may be useful but not necessary.

Focus on Projects

Write about your projects – the important parts. Include links to achievements or results. When choosing which projects to include, remember to prioritize any projects that solved real-world problems.

Include Online Profiles

This could as basic as your LinkedIn profile or more technical like your profiles on GitHub or Kaggle. Of course, these profiles must be complete with descriptions of your work that the employer may find useful.

For instance, it is not only important to demonstrate what you developed, but also how you developed it. A ‘readme’ file that explains your project, how it works, etc is ideal.

Highlight Knowledge Sharing

Include links to your work on any knowledge sharing platform like Medium, Quora, Stack Overflow, or your own blog for that matter. I’ve written before about the importance of blogging and writing for your data science career. Employers like to come across candidates, who engage with the data science community – who help others learn and grow actively. They like to see what the candidate has shared publicly, and how they interact with other data scientists.

How You Can Land the Best Data Science Jobs

Create a public proof of your work – also known as a portfolio. Include anything in this that may count as real-world experience. This could be an independent project that you may have undertaken or work that you did for the public’s benefit. Kaggle competitions, capstone projects and college theses also count!

It is important to also pay attention to your resume. They often tend to be gateway to your data science portfolio for hiring managers. Keep your resume short and to the point. Highlight all relevant information regarding course work and projects. Exclude everything superfluous like your objective.

Describe your experiences completely. Include links that may be useful. And, focus on your achievements and results.

Keep improving your data science portfolio as you progress in your career. Update it regularly so that prospective clients know all about your valuable capabilities.